Overview

Dataset statistics

Number of variables14
Number of observations12349
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory120.0 B

Variable types

Text2
Numeric12

Alerts

percentage0to15years is highly overall correlated with percentagehouseholdswithchildrenHigh correlation
percentage25to45years is highly overall correlated with percentage45to65years and 4 other fieldsHigh correlation
percentage45to65years is highly overall correlated with percentage25to45years and 1 other fieldsHigh correlation
percentage65yearsorolder is highly overall correlated with percentage25to45yearsHigh correlation
percentagehouseholdswithchildren is highly overall correlated with percentage0to15years and 1 other fieldsHigh correlation
percentagehouseholdswithoutchildren is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentagenonwesternmigrationbackground is highly overall correlated with percentage25to45years and 4 other fieldsHigh correlation
percentageonepersonhouseholds is highly overall correlated with percentagehouseholdswithchildren and 3 other fieldsHigh correlation
percentagewesternmigrationbackground is highly overall correlated with percentagenonwesternmigrationbackground and 1 other fieldsHigh correlation
populationdensityperkm2 is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
neighborhoodcode has unique valuesUnique
percentagenonwesternmigrationbackground has 1332 (10.8%) zerosZeros

Reproduction

Analysis started2024-07-05 09:50:06.680007
Analysis finished2024-07-05 09:50:32.399204
Duration25.72 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

neighborhoodcode
Text

UNIQUE 

Distinct12349
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:32.637061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters123490
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12349 ?
Unique (%)100.0%

Sample

1st rowBU00030000
2nd rowBU00030001
3rd rowBU00030002
4th rowBU00030007
5th rowBU00030008
ValueCountFrequency (%)
bu00030000 1
 
< 0.1%
bu00340110 1
 
< 0.1%
bu00100306 1
 
< 0.1%
bu00030002 1
 
< 0.1%
bu00030007 1
 
< 0.1%
bu00030008 1
 
< 0.1%
bu00030009 1
 
< 0.1%
bu00100101 1
 
< 0.1%
bu00100202 1
 
< 0.1%
bu00100203 1
 
< 0.1%
Other values (12339) 12339
99.9%
2024-07-05T11:50:33.515744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35551
28.8%
1 14241
11.5%
B 12349
 
10.0%
U 12349
 
10.0%
2 7599
 
6.2%
3 7519
 
6.1%
9 6452
 
5.2%
4 6061
 
4.9%
5 5797
 
4.7%
6 5282
 
4.3%
Other values (2) 10290
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98792
80.0%
Uppercase Letter 24698
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35551
36.0%
1 14241
14.4%
2 7599
 
7.7%
3 7519
 
7.6%
9 6452
 
6.5%
4 6061
 
6.1%
5 5797
 
5.9%
6 5282
 
5.3%
7 5218
 
5.3%
8 5072
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 12349
50.0%
U 12349
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98792
80.0%
Latin 24698
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35551
36.0%
1 14241
14.4%
2 7599
 
7.7%
3 7519
 
7.6%
9 6452
 
6.5%
4 6061
 
6.1%
5 5797
 
5.9%
6 5282
 
5.3%
7 5218
 
5.3%
8 5072
 
5.1%
Latin
ValueCountFrequency (%)
B 12349
50.0%
U 12349
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35551
28.8%
1 14241
11.5%
B 12349
 
10.0%
U 12349
 
10.0%
2 7599
 
6.2%
3 7519
 
6.1%
9 6452
 
5.2%
4 6061
 
4.9%
5 5797
 
4.7%
6 5282
 
4.3%
Other values (2) 10290
 
8.3%
Distinct11471
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:33.881009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length50
Mean length15.694145
Min length2

Characters and Unicode

Total characters193807
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11038 ?
Unique (%)89.4%

Sample

1st rowAppingedam-Centrum
2nd rowAppingedam-West
3rd rowAppingedam-Oost
4th rowVerspreide huizen Damsterdiep en Eemskanaal
5th rowVerspreide huizen ten zuiden van Eemskanaal
ValueCountFrequency (%)
verspreide 1300
 
5.8%
huizen 1294
 
5.8%
de 793
 
3.5%
en 624
 
2.8%
buitengebied 478
 
2.1%
omgeving 248
 
1.1%
noord 234
 
1.0%
kern 228
 
1.0%
zuid 214
 
1.0%
west 183
 
0.8%
Other values (9587) 16760
75.0%
2024-07-05T11:50:34.395699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 30978
16.0%
r 15023
 
7.8%
n 13249
 
6.8%
i 11653
 
6.0%
10007
 
5.2%
o 9862
 
5.1%
t 9000
 
4.6%
d 8902
 
4.6%
u 8569
 
4.4%
s 7773
 
4.0%
Other values (71) 68791
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159568
82.3%
Uppercase Letter 20616
 
10.6%
Space Separator 10007
 
5.2%
Dash Punctuation 2351
 
1.2%
Other Punctuation 600
 
0.3%
Decimal Number 484
 
0.2%
Open Punctuation 89
 
< 0.1%
Close Punctuation 89
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30978
19.4%
r 15023
9.4%
n 13249
 
8.3%
i 11653
 
7.3%
o 9862
 
6.2%
t 9000
 
5.6%
d 8902
 
5.6%
u 8569
 
5.4%
s 7773
 
4.9%
a 7762
 
4.9%
Other values (25) 36797
23.1%
Uppercase Letter
ValueCountFrequency (%)
B 2336
 
11.3%
V 2117
 
10.3%
H 1450
 
7.0%
D 1446
 
7.0%
W 1371
 
6.7%
O 1282
 
6.2%
S 1216
 
5.9%
N 1173
 
5.7%
Z 1086
 
5.3%
K 1032
 
5.0%
Other values (15) 6107
29.6%
Decimal Number
ValueCountFrequency (%)
1 117
24.2%
2 98
20.2%
0 96
19.8%
3 88
18.2%
4 34
 
7.0%
5 21
 
4.3%
9 8
 
1.7%
6 8
 
1.7%
7 7
 
1.4%
8 7
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 267
44.5%
' 140
23.3%
, 104
 
17.3%
/ 79
 
13.2%
" 6
 
1.0%
& 4
 
0.7%
Space Separator
ValueCountFrequency (%)
10007
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2351
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180184
93.0%
Common 13623
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30978
17.2%
r 15023
 
8.3%
n 13249
 
7.4%
i 11653
 
6.5%
o 9862
 
5.5%
t 9000
 
5.0%
d 8902
 
4.9%
u 8569
 
4.8%
s 7773
 
4.3%
a 7762
 
4.3%
Other values (50) 57413
31.9%
Common
ValueCountFrequency (%)
10007
73.5%
- 2351
 
17.3%
. 267
 
2.0%
' 140
 
1.0%
1 117
 
0.9%
, 104
 
0.8%
2 98
 
0.7%
0 96
 
0.7%
( 89
 
0.7%
) 89
 
0.7%
Other values (11) 265
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193736
> 99.9%
None 71
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30978
16.0%
r 15023
 
7.8%
n 13249
 
6.8%
i 11653
 
6.0%
10007
 
5.2%
o 9862
 
5.1%
t 9000
 
4.6%
d 8902
 
4.6%
u 8569
 
4.4%
s 7773
 
4.0%
Other values (62) 68720
35.5%
None
ValueCountFrequency (%)
ë 29
40.8%
â 20
28.2%
û 6
 
8.5%
é 6
 
8.5%
ö 4
 
5.6%
ï 3
 
4.2%
ú 1
 
1.4%
ô 1
 
1.4%
á 1
 
1.4%

populationdensityperkm2
Real number (ℝ)

HIGH CORRELATION 

Distinct6016
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3316.7439
Minimum2
Maximum52173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:34.599050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q1170
median2110
Q35150
95-th percentile10236
Maximum52173
Range52171
Interquartile range (IQR)4980

Descriptive statistics

Standard deviation4014.3476
Coefficient of variation (CV)1.2103279
Kurtosis9.7819752
Mean3316.7439
Median Absolute Deviation (MAD)2047
Skewness2.3756664
Sum40958470
Variance16114987
MonotonicityNot monotonic
2024-07-05T11:50:34.814767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 67
 
0.5%
14 62
 
0.5%
13 54
 
0.4%
22 51
 
0.4%
28 49
 
0.4%
19 48
 
0.4%
12 47
 
0.4%
18 47
 
0.4%
21 46
 
0.4%
20 46
 
0.4%
Other values (6006) 11832
95.8%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 8
 
0.1%
4 6
 
< 0.1%
5 18
0.1%
6 19
0.2%
7 30
0.2%
8 26
0.2%
9 35
0.3%
10 42
0.3%
11 43
0.3%
ValueCountFrequency (%)
52173 1
< 0.1%
36625 1
< 0.1%
35527 1
< 0.1%
34040 1
< 0.1%
33132 1
< 0.1%
32334 1
< 0.1%
31869 1
< 0.1%
31760 1
< 0.1%
31241 1
< 0.1%
30029 1
< 0.1%

percentage0to15years
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.892461
Minimum0
Maximum48
Zeros52
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:35.015698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median15
Q318
95-th percentile23
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.17913
Coefficient of variation (CV)0.34776858
Kurtosis1.7847744
Mean14.892461
Median Absolute Deviation (MAD)3
Skewness0.37788248
Sum183907
Variance26.823387
MonotonicityNot monotonic
2024-07-05T11:50:35.214624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
15 1254
 
10.2%
14 1180
 
9.6%
16 1157
 
9.4%
13 1035
 
8.4%
17 1027
 
8.3%
12 852
 
6.9%
18 792
 
6.4%
11 681
 
5.5%
19 620
 
5.0%
10 519
 
4.2%
Other values (34) 3232
26.2%
ValueCountFrequency (%)
0 52
 
0.4%
1 50
 
0.4%
2 70
 
0.6%
3 61
 
0.5%
4 105
 
0.9%
5 144
1.2%
6 152
1.2%
7 223
1.8%
8 278
2.3%
9 356
2.9%
ValueCountFrequency (%)
48 1
 
< 0.1%
44 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 2
 
< 0.1%
37 4
 
< 0.1%
36 10
0.1%
35 10
0.1%
34 21
0.2%

percentage15to25years
Real number (ℝ)

Distinct79
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.436473
Minimum0
Maximum97
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:35.414530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile19
Maximum97
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3460082
Coefficient of variation (CV)0.42986531
Kurtosis49.60762
Mean12.436473
Median Absolute Deviation (MAD)2
Skewness5.0800714
Sum153578
Variance28.579803
MonotonicityNot monotonic
2024-07-05T11:50:35.615251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 1795
14.5%
12 1603
13.0%
10 1565
12.7%
13 1311
10.6%
9 1029
8.3%
14 1026
8.3%
15 721
 
5.8%
8 611
 
4.9%
16 522
 
4.2%
7 359
 
2.9%
Other values (69) 1807
14.6%
ValueCountFrequency (%)
0 13
 
0.1%
1 13
 
0.1%
2 30
 
0.2%
3 39
 
0.3%
4 80
 
0.6%
5 114
 
0.9%
6 198
 
1.6%
7 359
 
2.9%
8 611
4.9%
9 1029
8.3%
ValueCountFrequency (%)
97 1
< 0.1%
96 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
78 1
< 0.1%
76 2
< 0.1%
75 1
< 0.1%
72 1
< 0.1%

percentage25to45years
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.197101
Minimum0
Maximum77
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:35.814374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q117
median21
Q325
95-th percentile38
Maximum77
Range77
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1477601
Coefficient of variation (CV)0.36706415
Kurtosis3.9347676
Mean22.197101
Median Absolute Deviation (MAD)4
Skewness1.4328937
Sum274112
Variance66.385994
MonotonicityNot monotonic
2024-07-05T11:50:36.014376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 914
 
7.4%
20 908
 
7.4%
19 854
 
6.9%
22 814
 
6.6%
18 811
 
6.6%
23 709
 
5.7%
17 694
 
5.6%
24 630
 
5.1%
16 613
 
5.0%
25 485
 
3.9%
Other values (63) 4917
39.8%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 8
 
0.1%
2 7
 
0.1%
3 11
 
0.1%
4 8
 
0.1%
5 22
 
0.2%
6 25
 
0.2%
7 37
0.3%
8 58
0.5%
9 87
0.7%
ValueCountFrequency (%)
77 1
 
< 0.1%
74 1
 
< 0.1%
72 4
< 0.1%
71 1
 
< 0.1%
69 2
 
< 0.1%
67 2
 
< 0.1%
66 2
 
< 0.1%
65 3
< 0.1%
64 1
 
< 0.1%
63 5
< 0.1%

percentage45to65years
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.96024
Minimum0
Maximum69
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:36.248033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q126
median30
Q334
95-th percentile40
Maximum69
Range69
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.6547116
Coefficient of variation (CV)0.2221181
Kurtosis1.6837857
Mean29.96024
Median Absolute Deviation (MAD)4
Skewness-0.34656144
Sum369979
Variance44.285186
MonotonicityNot monotonic
2024-07-05T11:50:36.448083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 878
 
7.1%
30 819
 
6.6%
27 792
 
6.4%
28 792
 
6.4%
31 791
 
6.4%
32 738
 
6.0%
33 696
 
5.6%
26 658
 
5.3%
34 604
 
4.9%
35 581
 
4.7%
Other values (51) 5000
40.5%
ValueCountFrequency (%)
0 11
0.1%
1 14
0.1%
2 10
0.1%
3 14
0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%
6 7
0.1%
7 7
0.1%
8 4
 
< 0.1%
9 15
0.1%
ValueCountFrequency (%)
69 1
 
< 0.1%
62 1
 
< 0.1%
60 1
 
< 0.1%
57 2
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
54 2
 
< 0.1%
53 2
 
< 0.1%
52 5
< 0.1%
51 6
< 0.1%

percentage65yearsorolder
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.515588
Minimum0
Maximum98
Zeros46
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:36.648837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median20
Q325
95-th percentile36
Maximum98
Range98
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.3565337
Coefficient of variation (CV)0.45606948
Kurtosis6.1623937
Mean20.515588
Median Absolute Deviation (MAD)5
Skewness1.4441401
Sum253347
Variance87.544724
MonotonicityNot monotonic
2024-07-05T11:50:36.863258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 717
 
5.8%
20 717
 
5.8%
18 694
 
5.6%
21 660
 
5.3%
17 647
 
5.2%
22 620
 
5.0%
23 575
 
4.7%
16 557
 
4.5%
15 499
 
4.0%
24 495
 
4.0%
Other values (78) 6168
49.9%
ValueCountFrequency (%)
0 46
 
0.4%
1 40
 
0.3%
2 52
 
0.4%
3 66
 
0.5%
4 77
 
0.6%
5 100
0.8%
6 119
1.0%
7 163
1.3%
8 178
1.4%
9 199
1.6%
ValueCountFrequency (%)
98 2
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
89 2
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%
86 1
 
< 0.1%
84 4
< 0.1%
83 1
 
< 0.1%
80 1
 
< 0.1%

percentageonepersonhouseholds
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.258466
Minimum0
Maximum100
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:37.047656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q122
median29
Q340
95-th percentile61
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.655467
Coefficient of variation (CV)0.45431381
Kurtosis1.5097841
Mean32.258466
Median Absolute Deviation (MAD)8
Skewness1.0859082
Sum398359.8
Variance214.7827
MonotonicityNot monotonic
2024-07-05T11:50:37.247977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 459
 
3.7%
25 457
 
3.7%
24 441
 
3.6%
28 431
 
3.5%
26 426
 
3.4%
29 413
 
3.3%
27 400
 
3.2%
30 399
 
3.2%
22 397
 
3.2%
31 394
 
3.2%
Other values (111) 8132
65.9%
ValueCountFrequency (%)
0 8
 
0.1%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 5
 
< 0.1%
4 10
 
0.1%
5 17
 
0.1%
6 25
0.2%
7 20
0.2%
8 35
0.3%
9 49
0.4%
ValueCountFrequency (%)
100 4
< 0.1%
99 2
 
< 0.1%
98 5
< 0.1%
97 2
 
< 0.1%
96 5
< 0.1%
95 2
 
< 0.1%
94 5
< 0.1%
93 3
< 0.1%
92 5
< 0.1%
91 6
< 0.1%

percentagehouseholdswithoutchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.227452
Minimum0
Maximum71
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:37.464299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q127
median32
Q337
95-th percentile46
Maximum71
Range71
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3742425
Coefficient of variation (CV)0.25984811
Kurtosis0.77328409
Mean32.227452
Median Absolute Deviation (MAD)5
Skewness0.10410673
Sum397976.8
Variance70.127938
MonotonicityNot monotonic
2024-07-05T11:50:37.670631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 653
 
5.3%
32 647
 
5.2%
34 647
 
5.2%
35 627
 
5.1%
31 615
 
5.0%
36 610
 
4.9%
30 571
 
4.6%
29 518
 
4.2%
37 510
 
4.1%
38 507
 
4.1%
Other values (86) 6444
52.2%
ValueCountFrequency (%)
0 7
0.1%
1 5
 
< 0.1%
2 8
0.1%
3 1
 
< 0.1%
4 9
0.1%
5 3
 
< 0.1%
6 17
0.1%
7 4
 
< 0.1%
8 7
0.1%
9 14
0.1%
ValueCountFrequency (%)
71 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
66 2
 
< 0.1%
65 2
 
< 0.1%
64 3
< 0.1%
63 2
 
< 0.1%
62 6
< 0.1%
61 6
< 0.1%
60 5
< 0.1%

percentagehouseholdswithchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.526342
Minimum0
Maximum94
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:37.870245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q129
median36
Q343
95-th percentile55
Maximum94
Range94
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.9223
Coefficient of variation (CV)0.33559041
Kurtosis0.70039781
Mean35.526342
Median Absolute Deviation (MAD)7
Skewness-0.092440425
Sum438714.8
Variance142.14123
MonotonicityNot monotonic
2024-07-05T11:50:38.064200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 524
 
4.2%
36 508
 
4.1%
38 506
 
4.1%
35 498
 
4.0%
39 494
 
4.0%
34 484
 
3.9%
40 471
 
3.8%
33 464
 
3.8%
41 425
 
3.4%
32 403
 
3.3%
Other values (101) 7572
61.3%
ValueCountFrequency (%)
0 26
0.2%
1 23
0.2%
1.8 1
 
< 0.1%
2 21
0.2%
3 21
0.2%
4 21
0.2%
5 38
0.3%
6 37
0.3%
7 52
0.4%
8 37
0.3%
ValueCountFrequency (%)
94 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
81 1
 
< 0.1%
80 2
 
< 0.1%
78 5
< 0.1%
77 6
< 0.1%
76 4
< 0.1%
75 4
< 0.1%
74 1
 
< 0.1%

percentagewesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0864847
Minimum0
Maximum83
Zeros117
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:38.247654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile21
Maximum83
Range83
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.5923189
Coefficient of variation (CV)0.72550817
Kurtosis14.698667
Mean9.0864847
Median Absolute Deviation (MAD)3
Skewness2.7112725
Sum112209
Variance43.458668
MonotonicityNot monotonic
2024-07-05T11:50:38.430513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1085
 
8.8%
5 1060
 
8.6%
6 1049
 
8.5%
8 1037
 
8.4%
4 1016
 
8.2%
9 876
 
7.1%
10 869
 
7.0%
3 792
 
6.4%
11 683
 
5.5%
12 555
 
4.5%
Other values (60) 3327
26.9%
ValueCountFrequency (%)
0 117
 
0.9%
1 253
 
2.0%
2 511
4.1%
3 792
6.4%
4 1016
8.2%
5 1060
8.6%
6 1049
8.5%
7 1085
8.8%
8 1037
8.4%
9 876
7.1%
ValueCountFrequency (%)
83 1
 
< 0.1%
76 1
 
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
73 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
< 0.1%

percentagenonwesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.457203
Minimum0
Maximum96
Zeros1332
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:38.630819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile33
Maximum96
Range96
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.640212
Coefficient of variation (CV)1.3763667
Kurtosis9.5149611
Mean8.457203
Median Absolute Deviation (MAD)3
Skewness2.758628
Sum104438
Variance135.49454
MonotonicityNot monotonic
2024-07-05T11:50:38.837176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1579
12.8%
2 1464
11.9%
0 1332
 
10.8%
3 1234
 
10.0%
4 942
 
7.6%
5 708
 
5.7%
6 580
 
4.7%
7 452
 
3.7%
8 371
 
3.0%
9 332
 
2.7%
Other values (80) 3355
27.2%
ValueCountFrequency (%)
0 1332
10.8%
1 1579
12.8%
2 1464
11.9%
3 1234
10.0%
4 942
7.6%
5 708
5.7%
6 580
 
4.7%
7 452
 
3.7%
8 371
 
3.0%
9 332
 
2.7%
ValueCountFrequency (%)
96 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
86 3
< 0.1%
85 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%

percentagemen
Real number (ℝ)

Distinct310
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.639623
Minimum28.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.0 KiB
2024-07-05T11:50:39.013287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum28.4
5-th percentile46
Q148.8
median50.2
Q352.1
95-th percentile56.2
Maximum100
Range71.6
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation3.7584243
Coefficient of variation (CV)0.074219043
Kurtosis22.932097
Mean50.639623
Median Absolute Deviation (MAD)1.6
Skewness2.5260485
Sum625348.7
Variance14.125753
MonotonicityNot monotonic
2024-07-05T11:50:39.264395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 954
 
7.7%
50.5 212
 
1.7%
49.7 204
 
1.7%
49.6 199
 
1.6%
49.5 196
 
1.6%
50.6 191
 
1.5%
50.8 191
 
1.5%
51.4 187
 
1.5%
49.4 186
 
1.5%
50.7 185
 
1.5%
Other values (300) 9644
78.1%
ValueCountFrequency (%)
28.4 1
 
< 0.1%
30 1
 
< 0.1%
31.3 1
 
< 0.1%
31.7 1
 
< 0.1%
33.3 3
< 0.1%
34 1
 
< 0.1%
34.2 1
 
< 0.1%
35.3 1
 
< 0.1%
35.7 1
 
< 0.1%
36.4 5
< 0.1%
ValueCountFrequency (%)
100 2
< 0.1%
96.7 1
< 0.1%
93.3 1
< 0.1%
92 1
< 0.1%
91.7 2
< 0.1%
89.5 1
< 0.1%
88.2 1
< 0.1%
87.5 1
< 0.1%
86.7 1
< 0.1%
86 1
< 0.1%

Interactions

2024-07-05T11:50:30.072313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:07.806360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.990629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.841860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:14.689267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.257666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.139859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.929953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:22.994864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.834947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.518325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.350919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.233052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.007483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.157115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.990086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:14.988637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.458243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.277843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.066451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.209551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.969276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.684556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.483957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.402560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.157881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.340628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:12.123044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:15.188775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.658917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.409374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.197964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.352379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.104314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.837238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.620176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.534109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.306229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.473839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:12.256447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:15.442998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.787007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.553846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.329414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.486113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.218299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.969801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.765979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.686888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.491381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.640466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:12.390323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:15.629512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.937174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.753890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.467454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.651096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.368921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.114020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.911149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.820220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.691431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.790058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:12.589091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:15.771705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.090905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:19.894961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.620321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.803002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.585262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.234105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.117678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:30.950329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:08.907100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:10.923344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:13.555623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:15.975074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.238010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.028081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.767946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:23.936042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.720221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.370158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.250141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:31.102082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.136906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.056761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:13.689268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:16.159985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.391685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.164577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:21.883889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.086295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:25.851634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.506465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.383857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:31.232714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.290734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.206441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:13.856000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:16.360462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.586928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.311755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:22.102637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.235065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.008133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.636910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.533139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:31.406175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.440596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.373662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:14.006589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:16.607094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.720595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.443232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:22.235753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.418571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.134509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.770440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.667480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:31.550181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.674041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.541453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:14.142125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:16.817103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.849642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.570184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:22.353192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.552432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.252335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:27.950783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.800965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:31.707936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:09.824093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:11.690106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:14.456586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:17.057247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:18.992705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:20.703803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:22.503673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:24.687199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:26.384790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:28.134853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:29.917235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T11:50:39.513482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentagehouseholdswithchildrenpercentagehouseholdswithoutchildrenpercentagemenpercentagenonwesternmigrationbackgroundpercentageonepersonhouseholdspercentagewesternmigrationbackgroundpopulationdensityperkm2
percentage0to15years1.0000.0400.265-0.218-0.4500.665-0.237-0.0270.072-0.369-0.2000.107
percentage15to25years0.0401.000-0.0520.150-0.4710.347-0.2120.262-0.068-0.182-0.159-0.115
percentage25to45years0.265-0.0521.000-0.589-0.532-0.102-0.592-0.0140.5780.3980.3620.527
percentage45to65years-0.2180.150-0.5891.0000.0250.2620.4750.332-0.498-0.461-0.274-0.506
percentage65yearsorolder-0.450-0.471-0.5320.0251.000-0.4540.485-0.290-0.2040.145-0.028-0.117
percentagehouseholdswithchildren0.6650.347-0.1020.262-0.4541.0000.0120.193-0.274-0.790-0.429-0.245
percentagehouseholdswithoutchildren-0.237-0.212-0.5920.4750.4850.0121.0000.136-0.553-0.529-0.355-0.474
percentagemen-0.0270.262-0.0140.332-0.2900.1930.1361.000-0.292-0.258-0.177-0.420
percentagenonwesternmigrationbackground0.072-0.0680.578-0.498-0.204-0.274-0.553-0.2921.0000.5290.5840.720
percentageonepersonhouseholds-0.369-0.1820.398-0.4610.145-0.790-0.529-0.2580.5291.0000.5400.471
percentagewesternmigrationbackground-0.200-0.1590.362-0.274-0.028-0.429-0.355-0.1770.5840.5401.0000.471
populationdensityperkm20.107-0.1150.527-0.506-0.117-0.245-0.474-0.4200.7200.4710.4711.000

Missing values

2024-07-05T11:50:31.912175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T11:50:32.215663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
0BU00030000Appingedam-Centrum2845.010.09.021.030.030.053.027.020.06.05.048.0
1BU00030001Appingedam-West1909.015.012.018.033.022.027.037.035.06.04.048.9
2BU00030002Appingedam-Oost1983.015.011.021.027.027.037.031.032.09.011.048.5
3BU00030007Verspreide huizen Damsterdiep en Eemskanaal60.019.014.021.036.09.014.035.051.07.02.053.8
4BU00030008Verspreide huizen ten zuiden van Eemskanaal19.013.015.010.048.013.017.044.039.01.01.052.4
5BU00030009Verspreide huizen ten noorden van het Damsterdiep21.016.014.017.035.018.017.040.043.05.02.051.6
6BU00100101Centrum2906.04.08.023.025.041.068.024.08.012.010.053.1
7BU00100202Over de Gracht4664.014.010.021.032.023.028.038.034.08.03.050.7
8BU00100203Scheepvaartbuurt2479.08.07.017.019.050.054.030.015.08.06.043.8
9BU00100204Steenbakkersbuurt2707.016.010.021.029.024.028.036.036.06.05.050.7
neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
13794BU19781605Oud-Alblas-Buitengebied-Noord15.023.023.015.031.08.09.018.073.02.02.047.4
13795BU19781701Schelluinen-Dorp3051.018.013.024.028.017.021.037.042.06.04.049.3
13799BU19781705Schelluinen-Buitengebied-Noordoost69.015.012.016.036.021.019.038.043.05.02.052.9
13800BU19781801Dijkgebied-Streefkerk536.019.020.019.028.015.018.034.048.02.00.051.5
13801BU19781802Streefkerk-Buitengebied17.017.028.018.031.07.011.026.062.00.01.053.2
13802BU19781803Streefkerk-Dorp3574.016.015.019.028.022.027.033.040.03.03.050.1
13803BU19781901Waal-Dorp2362.013.018.021.040.08.011.034.055.03.03.052.0
13805BU19782002Kern-Dorp3843.013.017.018.031.022.027.036.037.02.00.052.9
13806BU19782003Lintbebouwing-Oost550.025.014.012.025.023.010.032.059.01.04.050.0
13807BU19782004Lintbebouwing-West583.017.014.016.029.025.011.041.049.00.00.057.1